OBJECTIVE: The objective of this study is to create a zero-shot image denoising framework using a conditional generative adversarial networks (cGANs) for improving image quality and quantitative accuracy in non-gated and dual-gated cardiac PET images.
METHODS: Our study included retrospective list-mode data from 40 patients who underwent an 18F-fluorodeoxyglucose (18F-FDG) cardiac PET study. We initially trained and evaluated a 3D cGAN-known as Pix2Pix-on simulated non-gated low-count PET data paired with corresponding full-count target data, and then deployed the model on an unseen test set acquired on the same PET/CT system including both non-gated and dual-gated PET data.
RESULTS: Quantitative analysis demonstrated that the 3D Pix2Pix network architecture achieved significantly (p value<0.05) enhanced image quality and accuracy in both non-gated and gated cardiac PET images. At 5%, 10%, and 15% preserved count statistics, the model increased peak signal-to-noise ratio (PSNR) by 33.7%, 21.2%, and 15.5%, structural similarity index (SSIM) by 7.1%, 3.3%, and 2.2%, and reduced mean absolute error (MAE) by 61.4%, 54.3%, and 49.7%, respectively. When tested on dual-gated PET data, the model consistently reduced noise, irrespective of cardiac/respiratory motion phases, while maintaining image resolution and accuracy. Significant improvements were observed across all gates, including a 34.7% increase in PSNR, a 7.8% improvement in SSIM, and a 60.3% reduction in MAE.
CONCLUSIONS: The findings of this study indicate that dual-gated cardiac PET images, which often have post-reconstruction artifacts potentially affecting diagnostic performance, can be effectively improved using a generative pre-trained denoising network.
目的:本研究的目的是使用条件生成对抗网络(cGAN)创建零拍摄图像去噪框架,以提高非门控和双门控心脏PET图像的图像质量和定量准确性。
方法:我们的研究包括40例接受18F-氟代脱氧葡萄糖(18F-FDG)心脏PET研究的患者的回顾性列表模式数据。我们最初训练并评估了3DcGAN,称为Pix2Pix-on模拟的非门控低计数PET数据与相应的全计数目标数据配对,然后将模型部署在同一PET/CT系统上采集的未知测试集上,包括非门控和双门控PET数据。
结果:定量分析表明,3DPix2Pix网络架构在非门控和门控心脏PET图像中均实现了显着(p值<0.05)增强的图像质量和准确性。5%,10%,和15%保留的计数统计数据,该模型将峰值信噪比(PSNR)提高了33.7%,21.2%,和15.5%,结构相似性指数(SSIM)下降7.1%,3.3%,和2.2%,平均绝对误差(MAE)减少61.4%,54.3%,49.7%,分别。当在双门PET数据上测试时,该模型持续降低了噪音,不考虑心脏/呼吸运动阶段,同时保持图像分辨率和准确性。在所有大门上都观察到了显著的改善,包括PSNR增加34.7%,SSIM改善7.8%,MAE减少60.3%。
结论:这项研究的结果表明,双门控心脏PET图像,通常具有可能影响诊断性能的重建后伪影,可以使用生成预训练去噪网络有效地改进。